Machine Learning Online Training

Machine learning  Overview

Machine learning is being used in a wide range of applications today. One of the most well-known examples is Facebook's News Feed. The News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling to read or like a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.

Machine learning is also entering an array of enterprise applications. Customer relationship management (CRM) systems use learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses. Business intelligence (BI) and analytics vendors use machine learning in their software to help users automatically identify potentially important data points. Human resource (HR) systems use learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions.

Machine learning also plays an important role in self-driving cars. Deep learning neural networks are used to identify objects and determine optimal actions for safely steering a vehicle down the road.

Data Preprocessing

• Overview of Data Preprocessing
• Get the dataset
• Importing the Libraries
• Importing the Dataset
• Missing Data
• Categorical Data
• Splitting the Dataset into the Training set and Test set
• Feature Scaling
• How to Set Up Working Directory

Regression

Simple Linear Regression

• How to get the dataset
• Dataset + Business Problem Description
• Simple Linear Regression Intuition
• Simple Linear Regression in Python
• Simple Linear Regression in R

Multiple Linear Regression

• How to get the dataset
• Dataset + Business Problem Description
• Multiple Linear Regression Intuition
• Multiple Linear Regression in Python
• Multiple Linear Regression in Python – Backward Elimination – Preparation
• Multiple Linear Regression in R

Polynomial Regression

• Polynomial Regression Intuition
• How to get the dataset
• Polynomial Regression in Python
• Python Regression Template
• Polynomial Regression in R
• R Regression Template

Support Vector Regression (SVR)

• How to get the dataset
• SVR in Python
• SVR in R

Decision Tree Regression

• Decision Tree Regression Intuition
• How to get the dataset
• Decision Tree Regression in Python
• Decision Tree Regression in R

Random Forest Regression

• Random Forest Regression Intuition
• How to get the dataset
• Random Forest Regression in Python
• Random Forest Regression in R

Evaluating Regression Models Performance

• R-Squared Intuition
• Adjusted R-Squared Intuition
• Interpreting Linear Regression Coefficients

...and more

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No. of Days:	45
Total Hours:	45
No. of Participants:	5